Evidence map›Paper›PMID 24876583›Full record

ArticleJournal of diabetes science and technology2014

Design of the Glucose Rate Increase Detector: A Meal Detection Module for the Health Monitoring System.

Rebecca A Harvey, Eyal Dassau, Howard Zisser, Dale E Seborg, Francis J Doyle

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 65 citations in OpenAlex.

  1. Trial
  2. Efficacy ofDiabetes technology & therapeutics · 2020
    Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Data-driven meal events detection using blood glucose response patterns.BMC medical informatics and decision making · 2023
    Article
  16. Article
  17. Insulin Delivery Hardware: Pumps and Pens.Diabetes technology & therapeutics · 2023
    Article
  18. Article
  19. Article
  20. A New Meal Absorption Model for Artificial Pancreas Systems.Journal of diabetes science and technology · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Rebecca A HarveyDepartment of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Eyal DassauDepartment of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Biomolecular Science & Engineering Program, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Howard ZisserDepartment of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Dale E SeborgDepartment of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Francis J DoyleDepartment of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA Biomolecular Science & Engineering Program, University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA frank.doyle@icb.ucsb.edu.
University of California, Santa Barbara · USSansum Diabetes Research Institute · US

Funding

Ambulatory Artificial Pancreas: merging physiology, behavior, and control designDP3DK094331 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BASU, ANANDA, DOYLE, FRANCIS J · 2011 to 2014
$4.6M
Closed-loop Artificial Pancreas: Algorithm Engineering and Clinical EvaluationR01DK085628 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DOYLE, FRANCIS J, JOVANOVIC, LOIS · 2009 to 2013
$3.4M
NIDDK NIH HHS DP3 DK094331NIDDK NIH HHS R01 DK085628
6 · The paper itself

Abstract

The Glucose Rate Increase Detector (GRID), a module of the Health Monitoring System (HMS), has been designed to operate in parallel to the glucose controller to detect meal events and safely trigger a meal bolus. The GRID algorithm was tuned on clinical data with 40-70 g CHO meals and tested on simulation data with 50-100 g CHO meals. Active closed- and open-loop protocols were executed in silico with various treatments, including automatic boluses based on a 75 g CHO meal and boluses based on simulated user input of meal size. An optional function was used to reduce the recommended bolus using recent insulin and glucose history. For closed-loop control of a 3-meal scenario (50, 75, and 100 g CHO), the GRID improved median time in the 80-180 mg/dL range by 17% and in the >180 range by 14% over unannounced meals, using an automatic bolus for a 75 g CHO meal at detection. Under open-loop control of a 75 g CHO meal, the GRID shifted the median glucose peak down by 73 mg/dL and earlier by 120 min and reduced the time >180 mg/dL by 57% over a missed-meal bolus scenario, using a full meal bolus at detection. The GRID improved closed-loop control in the presence of large meals, without increasing late postprandial hypoglycemia. Users of basal-bolus therapy could also benefit from GRID as a safety alert for missed meal corrections.

Indexed as

artificial pancreasHealth Monitoring Systemmeal detectionsafety

Identifiers

PMID24876583
PMCPMC4455414
OpenAlexW2137448120

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.